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Record W2014659390 · doi:10.1002/masy.200450235

The influence of the counter‐ion MeB(C<sub>6</sub>F<sub>5</sub>)<sub>3</sub><sup>−</sup> and solvent effects on ethylene polymerization catalyzed by [(CpSiMe<sub>2</sub>NR)TiMe]<sup>+</sup>: a combined density functional theory and molecular mechanism study

2004· article· en· W2014659390 on OpenAlexaff
Zhitao Xu, Kumar Vanka, Tom Ziegler

Bibliographic record

VenueMacromolecular Symposia · 2004
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEthyleneCatalysisSolventActivation energyMonomerDensity functional theoryPhysical chemistryMaterials scienceIonPolymerizationChemistryPolymer chemistryCrystallographyComputational chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract A combined density functional theory and molecular mechanism (QM/MM) method has been used to study the first (R = Me) and the second (R = propyl) insertion of the ethylene monomer into the Ti‐R bond of (CpSiMe2Nt Bu)(R)Ti‐m‐MeB(C6F5)3. The computational results, in agreement with experimental findings, show the influence of the counter‐ion and the solvent not only changes the reaction barriers, but also alters the rate‐determining step for the second ethylene propagation, from insertion to uptake. The total reaction barrier for the first ethylene propagation has been calculated to be slightly higher than that for the second ethylene propagation, in general agreement with experimental results. The total barriers in solution are 11.7 kcal/mol and 10.5 kcal/mol for the first and second ethylene insertions, respectively. These insertion barriers are comparable to the activation energy (13.3 kcal/mol) found experimentally for the same catalyst (CGC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.176
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2004
Admission routes1
Has abstractyes

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